Military Targeting in the Context of Self-Defence Actions
Bibliographic record
Abstract
For self-defence actions to be lawful, they must be directed at military targets. The absolute prohibition on non-military targeting under the jus in bello is well known, but the jus ad bellum also limits the target selection of states conducting defensive operations. Restrictions on targeting form a key aspect of the customary international law criteria of necessity and proportionality. In most situations, the jus in bello will be the starting point for the definition of a military targeting rule. Yet it has been argued that there may be circumstances when the jus ad bellum and the jus in bello do not temporally or substantively overlap in situations of self-defence. In order to address any possible gaps in civilian protection, and to bring conceptual clarity to one particular dimension of the relationship between the two regimes, this article explores the independent sources of a military targeting rule. The aim is not to displace the jus in bello as the ‘lead’ regime on how targeting decisions must be made, or to undermine the traditional separation between the two ‘war law’ regimes. Rather, conceptual light is shed on a sometimes assumed but generally neglected dimension of the jus ad bellum’s necessity and proportionality criteria that may, in limited circumstances, have significance for our understanding of human protection during war.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".